Descriptives statistics, distributions, correlations, and reliability estimates
Driving simulation metrics
Overall

| speed_overall |
-0.04 |
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| distance_overall |
0.24 |
0.04 |
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| distance_overall_deviation |
0.24 |
0.04 |
1.00*** |
## Some items ( 2 4 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' optionSome items ( 2 4 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
## [[1]]
## [1] "collisions = 0.83"
##
## [[2]]
## [1] "speed = 0.88"
##
## [[3]]
## [1] "distance = 0.17"
##
## [[4]]
## [1] "distance_deviation = 0.17"
Fog-free periods

| speed_no_fog_overall |
0.34* |
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| distance_no_fog_overall |
0.24 |
-0.03 |
## Some items ( 1 5 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
## [[1]]
## [1] "collisions_no_fog = 0.81"
##
## [[2]]
## [1] "speed_no_fog = 0.75"
##
## [[3]]
## [1] "distance_no_fog = 0.04"
Fog event probe

| speed_fog_overall |
-0.05 |
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| distance_fog_overall |
0.27* |
0.08 |
## Some items ( 2 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
## [[1]]
## [1] "collisions_fog = 0.66"
##
## [[2]]
## [1] "speed_fog = 0.77"
##
## [[3]]
## [1] "distance_fog = -0.05"
Psychometric measures
Overall
| agreeableness |
3.9189815 |
0.6319277 |
3.98 |
0.63 |
3.86 |
0.63 |
| bias |
3.0441277 |
20.5145140 |
3.12 |
19.66 |
2.96 |
21.52 |
| confidence |
60.1323343 |
19.0227797 |
58.37 |
19.54 |
61.89 |
18.51 |
| congruent_errors |
0.9345794 |
1.3823075 |
0.93 |
1.55 |
0.94 |
1.20 |
| congruent_time |
461.5544460 |
51.0173810 |
448.12 |
50.44 |
475.24 |
48.31 |
| conscientiousness |
3.2384259 |
0.7499099 |
3.10 |
0.76 |
3.38 |
0.72 |
| discrimination |
27.2903277 |
19.4473443 |
28.85 |
19.66 |
25.74 |
19.29 |
| driving_years |
2.7171296 |
4.9146936 |
3.28 |
4.75 |
2.16 |
5.06 |
| extraversion |
3.1759259 |
0.7138968 |
3.18 |
0.73 |
3.17 |
0.70 |
| gaming_time |
1.1231481 |
1.0136320 |
1.26 |
1.05 |
0.99 |
0.96 |
| gf_accuracy |
57.0882066 |
21.9362721 |
55.25 |
21.92 |
58.93 |
22.00 |
| incongruent_errors |
1.5420561 |
1.8185645 |
1.63 |
1.78 |
1.45 |
1.87 |
| incongruent_time |
532.1606055 |
59.6359822 |
514.92 |
62.99 |
549.72 |
50.82 |
| inhibitory_cost |
70.6061595 |
30.0274495 |
66.80 |
31.09 |
74.49 |
28.68 |
| intellect |
3.6018519 |
0.6654324 |
3.56 |
0.65 |
3.64 |
0.69 |
| neuroticism |
2.7962963 |
0.7323852 |
2.82 |
0.75 |
2.77 |
0.72 |
| repeat_errors |
1.6574074 |
1.7356193 |
1.78 |
1.66 |
1.54 |
1.82 |
| repeat_time |
920.7157111 |
244.1991035 |
930.65 |
241.58 |
910.78 |
248.65 |
| resilience |
3.6137500 |
0.3568200 |
3.64 |
0.33 |
3.59 |
0.38 |
| switch_cost |
125.2960244 |
144.8566483 |
103.81 |
116.58 |
146.79 |
166.82 |
| switch_errors |
2.0370370 |
2.1352665 |
2.11 |
2.00 |
1.96 |
2.28 |
| switch_time |
1046.0117355 |
304.7873181 |
1034.45 |
289.04 |
1057.57 |
322.07 |
| wm_accuracy |
43.2716049 |
18.5563131 |
40.86 |
19.43 |
45.68 |
17.49 |

| bias |
-0.07 |
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| confidence |
0.01 |
0.39*** |
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| congruent_errors |
0.11 |
0.18 |
0.03 |
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| congruent_time |
0.03 |
-0.18 |
-0.48*** |
-0.11 |
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| conscientiousness |
0.06 |
0.09 |
-0.05 |
-0.06 |
0.17 |
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| discrimination |
-0.07 |
-0.35*** |
0.03 |
-0.09 |
-0.17 |
-0.01 |
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| driving_years |
0.05 |
-0.29** |
-0.25* |
-0.09 |
0.28** |
0.13 |
-0.04 |
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| extraversion |
-0.03 |
0.09 |
-0.03 |
-0.10 |
0.10 |
-0.04 |
-0.06 |
0.05 |
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| gaming_time |
0.16 |
-0.07 |
0.22* |
-0.16 |
-0.17 |
-0.22* |
0.24* |
-0.17 |
-0.18 |
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| gf_accuracy |
0.07 |
-0.60*** |
0.51*** |
-0.14 |
-0.24* |
-0.12 |
0.36*** |
0.06 |
-0.11 |
0.26** |
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| incongruent_errors |
0.04 |
0.14 |
0.14 |
0.26** |
-0.52*** |
-0.19 |
0.08 |
-0.19 |
-0.04 |
0.07 |
-0.01 |
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| incongruent_time |
-0.03 |
-0.19 |
-0.47*** |
-0.21* |
0.86*** |
0.14 |
-0.13 |
0.26** |
0.03 |
-0.17 |
-0.23* |
-0.41*** |
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| inhibitory_cost |
-0.11 |
-0.07 |
-0.11 |
-0.23* |
0.02 |
-0.02 |
0.01 |
0.04 |
-0.11 |
-0.04 |
-0.03 |
0.07 |
0.52*** |
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| intellect |
-0.06 |
-0.05 |
0.20* |
0.07 |
-0.10 |
-0.17 |
0.14 |
-0.04 |
-0.06 |
0.29** |
0.21* |
0.07 |
-0.09 |
0.00 |
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| neuroticism |
0.04 |
-0.12 |
0.02 |
0.08 |
0.03 |
0.02 |
0.09 |
-0.04 |
0.00 |
-0.10 |
0.13 |
0.12 |
0.05 |
0.04 |
0.07 |
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| repeat_errors |
-0.11 |
0.16 |
-0.13 |
0.22* |
-0.15 |
-0.24* |
-0.16 |
-0.13 |
-0.09 |
-0.15 |
-0.27** |
0.43*** |
-0.09 |
0.08 |
-0.02 |
0.03 |
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| repeat_time |
-0.02 |
-0.09 |
-0.37*** |
-0.14 |
0.51*** |
0.10 |
-0.10 |
0.15 |
0.10 |
-0.14 |
-0.24* |
-0.25* |
0.41*** |
-0.05 |
-0.03 |
0.17 |
-0.08 |
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| resilience |
0.08 |
-0.02 |
0.06 |
-0.12 |
-0.09 |
0.14 |
0.05 |
0.06 |
0.17 |
-0.04 |
0.07 |
0.08 |
-0.12 |
-0.08 |
-0.10 |
-0.37*** |
-0.01 |
-0.15 |
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| switch_cost |
-0.17 |
-0.29** |
-0.25** |
-0.10 |
0.38*** |
-0.02 |
0.08 |
0.20* |
0.09 |
-0.13 |
0.06 |
-0.25** |
0.32*** |
-0.01 |
-0.14 |
-0.08 |
-0.11 |
0.17 |
0.08 |
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| switch_errors |
-0.03 |
0.30** |
-0.18 |
0.25** |
-0.01 |
-0.18 |
-0.15 |
-0.13 |
-0.02 |
-0.10 |
-0.44*** |
0.30** |
0.02 |
0.06 |
-0.01 |
0.02 |
0.58*** |
-0.12 |
-0.07 |
-0.13 |
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| switch_time |
-0.10 |
-0.21* |
-0.42*** |
-0.16 |
0.59*** |
0.07 |
-0.04 |
0.21* |
0.13 |
-0.17 |
-0.16 |
-0.32*** |
0.48*** |
-0.04 |
-0.09 |
0.10 |
-0.12 |
0.88*** |
-0.09 |
0.61*** |
-0.16 |
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| wm_accuracy |
-0.03 |
0.04 |
0.36*** |
-0.08 |
-0.15 |
0.02 |
0.05 |
-0.13 |
-0.09 |
0.14 |
0.27** |
0.07 |
-0.14 |
-0.03 |
0.35*** |
0.10 |
-0.07 |
-0.14 |
0.06 |
0.07 |
-0.11 |
-0.08 |
Reliability overall
## [1] "resilience = 0.79"
## [1] "extraversion = 0.7"
## [1] "agreeableness = 0.59"
## [1] "conscientiousness = 0.65"
## [1] "neuroticism = 0.68"
## [1] "intellect = 0.61"
## [[1]]
## [1] "extraversion = 0.7"
##
## [[2]]
## [1] "agreeableness = 0.59"
##
## [[3]]
## [1] "conscientiousness = 0.65"
##
## [[4]]
## [1] "neuroticism = 0.68"
##
## [[5]]
## [1] "intellect = 0.61"
## [1] "RAPM accuracy = 0.83"
## [1] "RAPM confidence = 0.93"
## [1] "RAPM bias = 0.85"
## [1] "RAPM discrimination = 0.59"
## Some items ( 14 16 19 24 25 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "repeat errors = 0.49"
## Some items ( 1 2 6 7 10 11 12 14 16 18 20 22 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "switch errors = 0.39"
## [[1]]
## [1] "repeat errors = 0.49"
##
## [[2]]
## [1] "switch errors = 0.39"
## [1] "repeat time = 0.84"
## [1] "switch time = 0.87"
## [[1]]
## [1] "repeat time = 0.84"
##
## [[2]]
## [1] "switch time = 0.87"
## [1] "switch cost = 0.01"
## [1] "working memory accuracy = 0.7"
## Some items ( 1 4 6 7 8 9 11 12 13 14 15 16 17 19 23 25 28 30 31 32 34 40 44 45 46 49 55 56 60 63 67 71 72 79 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "congruent errors = 0.53"
## Some items ( 20 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "incongruent errors = 0.59"
## [[1]]
## [1] "congruent errors = 0.53"
##
## [[2]]
## [1] "incongruent errors = 0.59"
## [1] "congruent time = 0.98"
## [1] "incongruent time = 0.82"
## [[1]]
## [1] "congruent time = 0.98"
##
## [[2]]
## [1] "incongruent time = 0.82"
## [1] "inhibitory cost = 0.95"
Reliability for driver
## Some items ( 18 20 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "resilience = 0.77"
## [1] "extraversion = 0.76"
## [1] "agreeableness = 0.64"
## [1] "conscientiousness = 0.69"
## [1] "neuroticism = 0.68"
## [1] "intellect = 0.59"
## [[1]]
## [1] "extraversion = 0.76"
##
## [[2]]
## [1] "agreeableness = 0.64"
##
## [[3]]
## [1] "conscientiousness = 0.69"
##
## [[4]]
## [1] "neuroticism = 0.68"
##
## [[5]]
## [1] "intellect = 0.59"
## [1] "RAPM accuracy = 0.83"
## [1] "RAPM confidence = 0.94"
## [1] "RAPM bias = 0.84"
## [1] "RAPM discrimination = 0.45"
## Some items ( 2 3 4 8 9 13 14 17 19 21 23 24 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "switch errors = 0.3"
## Some items ( 2 3 4 5 7 8 9 10 11 12 13 14 19 21 22 26 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "repeat errors = 0.26"
## [[1]]
## [1] "switch errors = 0.3"
##
## [[2]]
## [1] "repeat errors = 0.26"
## [1] "switch time = 0.88"
## [1] "repeat time = 0.86"
## [[1]]
## [1] "switch time = 0.88"
##
## [[2]]
## [1] "repeat time = 0.86"
## [1] "switch cost = -0.21"
## Some items ( 15 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "working memory accuracy = 0.73"
## Some items ( 7 8 9 11 12 13 14 16 22 23 28 30 32 34 44 45 56 67 72 79 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "congruent errors = 0.64"
## Some items ( 1 6 11 17 20 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "incongruent errors = 0.56"
## [[1]]
## [1] "congruent errors = 0.64"
##
## [[2]]
## [1] "incongruent errors = 0.56"
## [1] "congruent time = 0.99"
## [1] "incongruent time = 0.86"
## [[1]]
## [1] "congruent time = 0.99"
##
## [[2]]
## [1] "incongruent time = 0.86"
## [1] "inhibitory cost = 0.97"
Reliability for codriver
## [1] "resilience = 0.82"
## [1] "extraversion = 0.65"
## [1] "agreeableness = 0.54"
## [1] "conscientiousness = 0.59"
## [1] "neuroticism = 0.68"
## [1] "intellect = 0.63"
## [[1]]
## [1] "extraversion = 0.65"
##
## [[2]]
## [1] "agreeableness = 0.54"
##
## [[3]]
## [1] "conscientiousness = 0.59"
##
## [[4]]
## [1] "neuroticism = 0.68"
##
## [[5]]
## [1] "intellect = 0.63"
## [1] "RAPM accuracy = 0.84"
## [1] "RAPM confidence = 0.92"
## [1] "RAPM bias = 0.85"
## [1] "RAPM discrimination = 0.72"
## Some items ( 6 9 10 11 12 14 16 17 18 19 24 25 26 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "repeat errors = 0.62"
## Some items ( 3 4 5 8 10 12 13 15 16 17 19 20 21 22 23 24 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "switch errors = 0.48"
## [[1]]
## [1] "repeat errors = 0.62"
##
## [[2]]
## [1] "switch errors = 0.48"
## [1] "repeat time = 0.82"
## [1] "switch time = 0.87"
## [[1]]
## [1] "repeat time = 0.82"
##
## [[2]]
## [1] "switch time = 0.87"
## [1] "switch cost = 0.09"
## [1] "working memory accuracy = 0.66"
## Some items ( 1 3 4 6 9 11 12 14 15 17 19 22 23 24 26 31 38 41 42 49 55 57 59 60 71 72 75 76 77 79 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "congruent errors = 0.36"
## Some items ( 2 11 19 20 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "incongruent errors = 0.64"
## [[1]]
## [1] "congruent errors = 0.36"
##
## [[2]]
## [1] "incongruent errors = 0.64"
## [1] "congruent time = 0.95"
## [1] "incongruent time = 0.74"
## [[1]]
## [1] "congruent time = 0.95"
##
## [[2]]
## [1] "incongruent time = 0.74"
## [1] "inhibitory cost = 0.93"
Communication measures

| co_info_harm_overall |
0.31* |
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| co_instruct_help_overall |
0.41** |
0.03 |
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| co_instruct_harm_overall |
0.13 |
0.43** |
0.48*** |
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| co_redundant_overall |
0.11 |
0.36** |
0.25 |
0.30* |
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| co_question_overall |
0.38** |
0.03 |
0.49*** |
0.45*** |
0.16 |
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| drive_question_overall |
0.63*** |
0.23 |
0.41** |
0.15 |
0.21 |
0.31* |
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| drive_informs_overall |
0.52*** |
0.17 |
0.45*** |
0.35** |
0.20 |
0.72*** |
0.38** |
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| drive_frust_overall |
0.14 |
0.39** |
0.30* |
0.30* |
0.33* |
0.11 |
0.28* |
0.26 |
Reliability estimates
## [[1]]
## [1] "co_info_help = 0.84"
##
## [[2]]
## [1] "co_info_harm = 0.73"
##
## [[3]]
## [1] "co_instruct_help = 0.9"
##
## [[4]]
## [1] "co_instruct_harm = 0.52"
##
## [[5]]
## [1] "co_redundant = 0.73"
##
## [[6]]
## [1] "co_question = 0.8"
##
## [[7]]
## [1] "drive_question = 0.8"
##
## [[8]]
## [1] "drive_informs = 0.85"
##
## [[9]]
## [1] "drive_frust = 0.89"
Given the moderate to high correlations between the communication variables, it looks like there is a multicollinearity issue. Need to reduce comms variables to a smaller number using factor analysis.
Regression analyses
Correlations between all variables
| speed_overall |
-0.04 |
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| distance_overall |
0.24 |
0.04 |
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| inconsistent_codriver |
0.28* |
-0.27* |
0.27* |
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| terrible_codriver |
0.32* |
-0.09 |
0.38** |
0.34* |
|
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|
| helpful_exchange |
0.07 |
-0.40** |
0.20 |
0.32* |
0.17 |
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| age_co_driver |
0.03 |
0.10 |
0.19 |
-0.02 |
-0.05 |
0.12 |
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| age_driver |
-0.01 |
-0.04 |
0.00 |
-0.11 |
0.07 |
0.20 |
-0.08 |
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| aus_born_co_driver |
-0.05 |
-0.09 |
0.07 |
0.20 |
-0.17 |
0.30* |
-0.12 |
0.02 |
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|
| aus_born_driver |
-0.03 |
0.01 |
-0.09 |
0.04 |
0.02 |
0.00 |
0.14 |
-0.27* |
-0.03 |
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| eng_fl_co_driver |
0.01 |
-0.22 |
0.11 |
0.18 |
-0.31* |
0.30* |
0.06 |
0.10 |
0.53*** |
0.01 |
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| eng_fl_driver |
-0.27 |
0.15 |
-0.10 |
-0.17 |
-0.23 |
-0.03 |
0.10 |
-0.19 |
0.09 |
0.40** |
0.11 |
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| sex_co_driver |
-0.22 |
0.08 |
-0.08 |
0.06 |
-0.04 |
0.05 |
-0.11 |
-0.07 |
0.07 |
0.14 |
-0.04 |
0.09 |
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| sex_driver |
-0.28* |
0.58*** |
0.07 |
-0.21 |
-0.13 |
-0.24 |
0.11 |
-0.19 |
0.06 |
0.00 |
-0.20 |
0.04 |
0.22 |
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|
| prop_female |
0.32* |
-0.47*** |
0.00 |
0.13 |
0.12 |
0.15 |
-0.02 |
0.18 |
-0.08 |
-0.07 |
0.16 |
-0.07 |
-0.70*** |
-0.85*** |
|
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|
| driving_years |
-0.08 |
-0.06 |
-0.03 |
-0.10 |
0.03 |
0.19 |
-0.07 |
0.95*** |
-0.03 |
-0.25 |
0.16 |
-0.07 |
-0.04 |
-0.26 |
0.22 |
|
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|
|
| gaming_time |
0.12 |
-0.01 |
0.24 |
0.05 |
0.09 |
0.19 |
-0.07 |
-0.05 |
0.08 |
-0.08 |
-0.13 |
-0.39** |
0.15 |
0.29* |
-0.29* |
-0.16 |
|
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|
|
| congruent_errors |
0.07 |
-0.13 |
-0.22 |
-0.17 |
-0.22 |
-0.18 |
0.00 |
-0.19 |
0.03 |
0.06 |
0.15 |
0.19 |
-0.08 |
-0.04 |
0.07 |
-0.17 |
-0.20 |
|
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|
|
| congruent_time |
0.04 |
-0.11 |
-0.18 |
0.01 |
0.00 |
0.08 |
-0.24 |
0.28* |
0.10 |
-0.04 |
0.09 |
-0.09 |
-0.09 |
-0.36** |
0.31* |
0.32* |
-0.29* |
-0.11 |
|
|
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|
|
|
| incongruent_errors |
0.37** |
-0.01 |
0.34* |
0.10 |
-0.01 |
0.03 |
0.42** |
-0.20 |
0.00 |
0.00 |
0.12 |
0.01 |
0.01 |
0.08 |
-0.06 |
-0.23 |
0.12 |
0.26 |
-0.47*** |
|
|
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|
|
|
| incongruent_time |
0.15 |
-0.03 |
-0.15 |
0.02 |
0.03 |
0.05 |
-0.17 |
0.25 |
0.09 |
-0.01 |
0.02 |
-0.07 |
-0.07 |
-0.36** |
0.30* |
0.28* |
-0.25 |
-0.22 |
0.87*** |
-0.38** |
|
|
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|
|
| inhibitory_cost |
0.24 |
0.11 |
0.00 |
0.01 |
0.06 |
-0.02 |
0.04 |
0.05 |
0.02 |
0.05 |
-0.11 |
0.00 |
0.01 |
-0.14 |
0.09 |
0.06 |
-0.03 |
-0.27 |
0.15 |
0.00 |
0.61*** |
|
|
|
|
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|
|
|
| repeat_errors |
0.21 |
-0.04 |
0.11 |
0.10 |
0.08 |
0.06 |
0.33* |
-0.21 |
-0.20 |
0.00 |
0.04 |
0.05 |
0.06 |
0.01 |
-0.04 |
-0.19 |
-0.19 |
0.21 |
-0.07 |
0.47*** |
-0.03 |
0.04 |
|
|
|
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|
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|
|
|
|
|
|
| repeat_time |
0.09 |
-0.05 |
-0.04 |
0.15 |
0.14 |
0.06 |
-0.18 |
0.09 |
0.10 |
0.08 |
0.02 |
-0.05 |
0.04 |
-0.21 |
0.13 |
0.14 |
-0.20 |
-0.13 |
0.62*** |
-0.28* |
0.48*** |
-0.03 |
-0.10 |
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
| switch_errors |
0.24 |
-0.05 |
-0.04 |
-0.12 |
0.00 |
-0.23 |
0.09 |
-0.25 |
-0.26 |
0.23 |
0.04 |
0.24 |
-0.08 |
-0.19 |
0.18 |
-0.22 |
-0.31* |
0.36** |
0.06 |
0.27 |
0.02 |
-0.07 |
0.43** |
-0.06 |
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
| switch_time |
0.02 |
0.00 |
-0.17 |
0.10 |
0.13 |
0.01 |
-0.17 |
0.16 |
0.02 |
0.02 |
-0.11 |
-0.05 |
-0.02 |
-0.13 |
0.11 |
0.20 |
-0.24 |
-0.21 |
0.70*** |
-0.39** |
0.57*** |
0.01 |
-0.14 |
0.92*** |
-0.08 |
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
| switch_cost |
-0.14 |
0.11 |
-0.34* |
-0.07 |
0.04 |
-0.09 |
-0.04 |
0.20 |
-0.14 |
-0.11 |
-0.30* |
-0.03 |
-0.12 |
0.10 |
-0.01 |
0.21 |
-0.18 |
-0.25 |
0.45*** |
-0.38** |
0.41** |
0.10 |
-0.13 |
0.21 |
-0.06 |
0.58*** |
|
|
|
|
|
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|
|
|
|
|
|
|
|
| wm_accuracy |
-0.12 |
-0.06 |
0.11 |
0.17 |
0.10 |
0.03 |
-0.13 |
0.02 |
0.06 |
-0.24 |
0.09 |
-0.04 |
0.15 |
0.13 |
-0.18 |
0.02 |
0.18 |
-0.11 |
-0.15 |
0.03 |
-0.08 |
0.08 |
-0.11 |
-0.24 |
-0.05 |
-0.19 |
0.03 |
|
|
|
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|
|
|
|
|
| resilience |
0.02 |
0.20 |
-0.03 |
0.16 |
0.24 |
0.11 |
-0.03 |
0.19 |
-0.20 |
-0.21 |
-0.09 |
-0.39** |
-0.03 |
0.22 |
-0.15 |
0.13 |
0.00 |
-0.14 |
-0.05 |
0.09 |
-0.08 |
-0.08 |
0.25 |
-0.21 |
-0.09 |
-0.09 |
0.21 |
0.26 |
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| gf_accuracy |
-0.14 |
0.15 |
0.16 |
0.12 |
0.11 |
0.10 |
0.09 |
0.20 |
0.03 |
-0.19 |
-0.07 |
-0.20 |
0.09 |
0.34* |
-0.29* |
0.20 |
0.40** |
-0.25 |
-0.32* |
-0.13 |
-0.21 |
0.10 |
-0.26 |
-0.18 |
-0.45*** |
-0.15 |
0.02 |
0.30* |
0.12 |
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
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|
|
|
|
|
|
| confidence |
-0.10 |
0.06 |
0.20 |
0.14 |
0.10 |
-0.05 |
0.14 |
-0.19 |
-0.21 |
-0.03 |
-0.10 |
-0.14 |
-0.06 |
0.20 |
-0.12 |
-0.22 |
0.38** |
-0.08 |
-0.57*** |
0.00 |
-0.49*** |
-0.07 |
-0.21 |
-0.32* |
-0.18 |
-0.39** |
-0.29* |
0.35* |
0.02 |
0.56*** |
|
|
|
|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
| bias |
0.06 |
-0.11 |
0.03 |
0.00 |
-0.02 |
-0.16 |
0.05 |
-0.42** |
-0.24 |
0.18 |
-0.03 |
0.09 |
-0.15 |
-0.17 |
0.21 |
-0.44*** |
-0.07 |
0.20 |
-0.21 |
0.14 |
-0.26 |
-0.18 |
0.08 |
-0.12 |
0.32* |
-0.22 |
-0.31* |
0.01 |
-0.10 |
-0.56*** |
0.37** |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| discrimination |
-0.06 |
0.21 |
0.08 |
-0.14 |
-0.13 |
-0.06 |
0.07 |
-0.10 |
0.01 |
0.10 |
-0.17 |
-0.06 |
0.34* |
0.38** |
-0.46*** |
-0.15 |
0.32* |
-0.14 |
-0.22 |
0.13 |
-0.20 |
-0.04 |
-0.13 |
-0.02 |
-0.06 |
-0.01 |
0.01 |
0.10 |
-0.05 |
0.32* |
0.11 |
-0.25 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| agreeableness |
0.12 |
-0.33* |
0.05 |
0.33* |
0.15 |
0.45*** |
-0.15 |
0.13 |
0.32* |
0.10 |
0.37** |
-0.05 |
-0.01 |
-0.29* |
0.22 |
0.14 |
0.13 |
0.15 |
0.06 |
0.06 |
0.01 |
-0.07 |
-0.07 |
0.07 |
0.03 |
-0.04 |
-0.24 |
0.06 |
0.08 |
0.17 |
0.04 |
-0.15 |
-0.04 |
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| conscientiousness |
-0.15 |
-0.07 |
0.05 |
0.02 |
0.05 |
0.02 |
-0.21 |
0.15 |
0.05 |
0.05 |
0.09 |
-0.09 |
-0.08 |
-0.03 |
0.07 |
0.15 |
-0.20 |
0.09 |
0.19 |
-0.11 |
0.13 |
-0.05 |
-0.15 |
0.06 |
0.00 |
0.04 |
-0.03 |
0.03 |
0.24 |
-0.13 |
-0.15 |
0.00 |
0.00 |
0.28* |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| extraversion |
-0.16 |
0.00 |
-0.08 |
0.12 |
-0.11 |
0.17 |
0.11 |
-0.01 |
-0.03 |
0.23 |
-0.10 |
0.23 |
-0.03 |
-0.19 |
0.15 |
0.07 |
-0.33* |
-0.17 |
0.12 |
-0.03 |
0.06 |
-0.07 |
-0.05 |
0.26 |
0.03 |
0.26 |
0.11 |
-0.15 |
-0.04 |
-0.23 |
-0.19 |
0.07 |
0.09 |
-0.11 |
-0.08 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| intellect |
0.04 |
0.04 |
-0.08 |
-0.22 |
0.03 |
-0.11 |
-0.06 |
0.01 |
0.11 |
-0.01 |
0.01 |
0.08 |
0.05 |
0.06 |
-0.07 |
0.01 |
0.23 |
0.28* |
-0.17 |
0.05 |
-0.10 |
0.07 |
-0.08 |
-0.12 |
0.05 |
-0.15 |
-0.13 |
0.40** |
-0.02 |
0.19 |
0.19 |
-0.02 |
0.11 |
0.07 |
-0.28* |
-0.24 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| neuroticism |
0.12 |
0.04 |
0.26 |
-0.05 |
-0.12 |
-0.18 |
0.09 |
-0.12 |
-0.02 |
-0.08 |
0.17 |
0.08 |
-0.18 |
-0.05 |
0.13 |
-0.04 |
-0.24 |
0.16 |
0.00 |
0.18 |
0.05 |
0.11 |
0.07 |
0.14 |
0.22 |
0.00 |
-0.28* |
-0.03 |
-0.34* |
0.02 |
-0.03 |
-0.06 |
0.09 |
-0.04 |
0.06 |
0.07 |
0.00 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| driving_years_drone |
0.05 |
0.11 |
0.21 |
0.03 |
-0.12 |
0.11 |
0.95*** |
-0.07 |
-0.07 |
0.09 |
0.18 |
0.09 |
-0.06 |
0.15 |
-0.07 |
-0.09 |
-0.07 |
0.03 |
-0.25 |
0.43** |
-0.19 |
0.02 |
0.32* |
-0.20 |
0.07 |
-0.21 |
-0.11 |
-0.12 |
0.08 |
0.07 |
0.14 |
0.06 |
0.12 |
-0.07 |
-0.15 |
0.06 |
-0.09 |
0.10 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| gaming_time_drone |
-0.05 |
0.00 |
-0.05 |
-0.10 |
-0.03 |
-0.05 |
-0.19 |
0.21 |
0.07 |
0.05 |
-0.08 |
-0.03 |
0.32* |
0.04 |
-0.20 |
0.20 |
0.21 |
-0.02 |
-0.21 |
-0.01 |
-0.11 |
0.12 |
0.04 |
-0.14 |
0.02 |
-0.22 |
-0.26 |
0.12 |
0.04 |
0.13 |
-0.04 |
-0.19 |
0.04 |
0.13 |
-0.12 |
0.01 |
0.24 |
0.03 |
-0.22 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| congruent_errors_drone |
-0.06 |
0.07 |
0.02 |
-0.09 |
-0.18 |
0.07 |
-0.02 |
0.13 |
0.12 |
-0.18 |
0.08 |
0.23 |
0.02 |
0.08 |
-0.07 |
0.21 |
-0.07 |
0.07 |
0.07 |
0.01 |
0.06 |
0.01 |
-0.22 |
0.03 |
-0.31* |
0.03 |
0.02 |
0.13 |
-0.16 |
-0.01 |
-0.07 |
-0.06 |
0.14 |
-0.18 |
0.05 |
0.14 |
0.05 |
0.08 |
0.00 |
-0.12 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| congruent_time_drone |
-0.06 |
-0.03 |
-0.06 |
-0.12 |
-0.26 |
-0.14 |
0.32* |
-0.06 |
-0.08 |
0.14 |
0.22 |
0.00 |
-0.19 |
0.00 |
0.10 |
-0.06 |
-0.03 |
0.20 |
-0.24 |
0.23 |
-0.18 |
0.02 |
0.11 |
-0.20 |
0.21 |
-0.25 |
-0.19 |
0.04 |
-0.09 |
-0.01 |
0.17 |
0.17 |
-0.19 |
-0.02 |
-0.02 |
-0.03 |
0.03 |
-0.02 |
0.34* |
0.02 |
-0.12 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| incongruent_errors_drone |
-0.07 |
0.04 |
0.07 |
0.06 |
0.11 |
0.01 |
-0.14 |
-0.06 |
-0.11 |
0.06 |
-0.21 |
0.14 |
0.30* |
0.11 |
-0.24 |
0.01 |
0.09 |
0.02 |
0.16 |
-0.08 |
0.05 |
-0.15 |
-0.03 |
0.14 |
-0.02 |
0.11 |
-0.02 |
-0.09 |
-0.11 |
0.14 |
-0.02 |
-0.17 |
0.26 |
0.04 |
0.01 |
-0.10 |
0.06 |
0.16 |
-0.16 |
0.00 |
0.27 |
-0.59*** |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| incongruent_time_drone |
0.01 |
-0.07 |
-0.02 |
-0.12 |
-0.28* |
-0.14 |
0.32* |
-0.10 |
0.00 |
0.07 |
0.21 |
-0.03 |
-0.23 |
0.05 |
0.09 |
-0.13 |
0.04 |
0.40** |
-0.31* |
0.38** |
-0.30* |
-0.10 |
0.06 |
-0.25 |
0.23 |
-0.32* |
-0.27* |
0.09 |
0.06 |
0.04 |
0.17 |
0.12 |
-0.11 |
0.17 |
0.10 |
-0.13 |
0.14 |
0.03 |
0.35* |
0.01 |
-0.22 |
0.83*** |
-0.47*** |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| inhibitory_cost_drone |
0.11 |
-0.07 |
0.06 |
-0.01 |
-0.05 |
-0.02 |
0.03 |
-0.08 |
0.13 |
-0.12 |
0.00 |
-0.05 |
-0.09 |
0.08 |
-0.01 |
-0.14 |
0.11 |
0.37** |
-0.15 |
0.29* |
-0.23 |
-0.22 |
-0.08 |
-0.10 |
0.06 |
-0.15 |
-0.17 |
0.10 |
0.24 |
0.09 |
0.03 |
-0.07 |
0.12 |
0.34* |
0.22 |
-0.18 |
0.21 |
0.09 |
0.05 |
-0.02 |
-0.19 |
-0.21 |
0.16 |
0.37** |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| repeat_errors_drone |
0.00 |
0.01 |
0.06 |
0.17 |
0.25 |
0.04 |
-0.12 |
-0.03 |
0.04 |
0.07 |
-0.05 |
0.14 |
0.06 |
0.11 |
-0.11 |
-0.01 |
-0.05 |
0.21 |
-0.02 |
0.13 |
-0.06 |
-0.08 |
0.09 |
0.04 |
-0.04 |
-0.01 |
-0.10 |
0.14 |
0.17 |
0.13 |
-0.08 |
-0.23 |
0.19 |
0.20 |
0.27* |
-0.05 |
0.10 |
0.27 |
-0.09 |
-0.13 |
0.23 |
-0.20 |
0.39** |
-0.11 |
0.13 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| repeat_time_drone |
0.01 |
0.26 |
0.13 |
-0.07 |
-0.02 |
0.03 |
0.23 |
-0.08 |
-0.15 |
-0.07 |
0.01 |
0.01 |
-0.13 |
0.28* |
-0.13 |
-0.07 |
0.04 |
-0.01 |
-0.21 |
0.23 |
-0.19 |
-0.04 |
0.25 |
-0.08 |
0.16 |
-0.11 |
-0.10 |
0.05 |
0.19 |
0.14 |
0.19 |
0.03 |
-0.26 |
-0.12 |
-0.05 |
-0.08 |
-0.04 |
0.05 |
0.15 |
-0.09 |
-0.16 |
0.46*** |
-0.22 |
0.41** |
-0.05 |
-0.07 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| switch_errors_drone |
-0.21 |
-0.15 |
0.17 |
0.19 |
0.07 |
0.03 |
-0.14 |
0.03 |
0.20 |
0.12 |
0.22 |
0.22 |
0.21 |
0.03 |
-0.14 |
0.08 |
-0.09 |
0.35* |
-0.09 |
0.15 |
-0.24 |
-0.35** |
0.06 |
0.01 |
0.02 |
-0.09 |
-0.25 |
0.15 |
0.04 |
0.10 |
-0.08 |
-0.19 |
0.17 |
0.31* |
0.13 |
-0.05 |
0.16 |
0.29* |
-0.06 |
0.08 |
0.15 |
-0.07 |
0.32* |
0.05 |
0.21 |
0.70*** |
-0.17 |
|
|
|
|
|
|
|
|
|
|
|
|
|
| switch_time_drone |
-0.01 |
0.31* |
0.09 |
0.01 |
-0.12 |
0.06 |
0.28* |
-0.06 |
-0.01 |
0.05 |
0.22 |
0.06 |
-0.12 |
0.26 |
-0.13 |
-0.04 |
0.06 |
-0.05 |
-0.20 |
0.27* |
-0.15 |
0.03 |
0.21 |
-0.03 |
0.09 |
-0.08 |
-0.13 |
0.09 |
0.10 |
0.19 |
0.24 |
0.03 |
-0.15 |
0.02 |
-0.02 |
-0.02 |
-0.02 |
0.04 |
0.24 |
-0.10 |
-0.11 |
0.51*** |
-0.25 |
0.43** |
-0.11 |
-0.10 |
0.86*** |
-0.22 |
|
|
|
|
|
|
|
|
|
|
|
|
| switch_cost_drone |
-0.04 |
0.20 |
-0.01 |
0.11 |
-0.21 |
0.08 |
0.20 |
0.00 |
0.20 |
0.21 |
0.42** |
0.09 |
-0.04 |
0.09 |
-0.04 |
0.04 |
0.07 |
-0.08 |
-0.07 |
0.19 |
0.00 |
0.12 |
0.04 |
0.07 |
-0.06 |
0.01 |
-0.11 |
0.10 |
-0.10 |
0.16 |
0.18 |
0.01 |
0.10 |
0.22 |
0.04 |
0.08 |
0.03 |
0.00 |
0.23 |
-0.06 |
0.03 |
0.30* |
-0.16 |
0.21 |
-0.13 |
-0.09 |
0.17 |
-0.17 |
0.65*** |
|
|
|
|
|
|
|
|
|
|
|
| wm_accuracy_drone |
-0.05 |
0.09 |
-0.12 |
-0.16 |
0.12 |
-0.12 |
-0.24 |
-0.02 |
-0.21 |
-0.10 |
-0.19 |
-0.10 |
0.18 |
0.02 |
-0.11 |
0.02 |
0.03 |
-0.23 |
0.05 |
-0.11 |
0.02 |
-0.03 |
-0.05 |
0.01 |
-0.01 |
0.04 |
0.07 |
0.15 |
0.01 |
0.07 |
0.08 |
0.00 |
-0.03 |
-0.07 |
-0.03 |
-0.06 |
0.03 |
-0.02 |
-0.26 |
0.13 |
-0.03 |
-0.24 |
0.13 |
-0.33* |
-0.19 |
-0.01 |
-0.03 |
-0.16 |
0.02 |
0.07 |
|
|
|
|
|
|
|
|
|
|
| resilience_drone |
-0.04 |
-0.22 |
0.18 |
0.14 |
0.19 |
0.28 |
-0.10 |
0.15 |
0.02 |
0.05 |
-0.01 |
-0.30* |
0.07 |
-0.19 |
0.11 |
0.10 |
0.26 |
-0.24 |
0.07 |
-0.16 |
-0.08 |
-0.26 |
-0.26 |
0.27 |
-0.28 |
0.26 |
0.09 |
-0.05 |
-0.11 |
0.04 |
0.12 |
0.07 |
0.17 |
0.12 |
0.06 |
0.12 |
-0.08 |
-0.22 |
-0.11 |
-0.11 |
-0.10 |
-0.10 |
0.07 |
-0.14 |
-0.07 |
-0.19 |
-0.12 |
-0.07 |
-0.09 |
0.01 |
-0.11 |
|
|
|
|
|
|
|
|
|
| gf_accuracy_drone |
0.16 |
-0.03 |
-0.01 |
-0.17 |
-0.07 |
-0.07 |
0.01 |
-0.19 |
-0.08 |
0.14 |
-0.12 |
0.09 |
0.08 |
-0.15 |
0.06 |
-0.13 |
0.07 |
-0.18 |
0.11 |
-0.03 |
0.22 |
0.26 |
0.03 |
0.09 |
0.14 |
0.09 |
0.04 |
-0.19 |
-0.36* |
-0.12 |
-0.08 |
0.06 |
0.09 |
-0.04 |
-0.20 |
0.11 |
-0.10 |
-0.07 |
-0.05 |
0.14 |
0.00 |
-0.23 |
0.13 |
-0.33* |
-0.20 |
-0.27* |
-0.29* |
-0.43** |
-0.19 |
0.06 |
0.23 |
0.04 |
|
|
|
|
|
|
|
|
| confidence_drone |
0.19 |
-0.05 |
-0.11 |
0.12 |
0.23 |
-0.09 |
-0.24 |
-0.13 |
-0.16 |
-0.02 |
-0.30* |
-0.21 |
0.19 |
-0.18 |
0.02 |
-0.13 |
-0.04 |
-0.06 |
0.24 |
-0.20 |
0.24 |
0.10 |
-0.12 |
0.21 |
-0.03 |
0.25 |
0.18 |
-0.08 |
-0.07 |
-0.26 |
-0.15 |
0.14 |
-0.03 |
-0.12 |
-0.03 |
-0.03 |
-0.06 |
-0.15 |
-0.26 |
0.08 |
0.19 |
-0.47*** |
0.31* |
-0.55*** |
-0.18 |
-0.05 |
-0.41** |
-0.18 |
-0.46*** |
-0.27 |
0.36** |
0.12 |
0.45*** |
|
|
|
|
|
|
|
| bias_drone |
0.01 |
-0.02 |
-0.08 |
0.28* |
0.27* |
-0.01 |
-0.22 |
0.09 |
-0.06 |
-0.16 |
-0.13 |
-0.27* |
0.08 |
0.00 |
-0.04 |
0.03 |
-0.10 |
0.13 |
0.10 |
-0.15 |
-0.01 |
-0.19 |
-0.13 |
0.09 |
-0.17 |
0.12 |
0.11 |
0.13 |
0.34* |
-0.10 |
-0.05 |
0.06 |
-0.12 |
-0.06 |
0.18 |
-0.14 |
0.05 |
-0.05 |
-0.17 |
-0.08 |
0.16 |
-0.17 |
0.13 |
-0.13 |
0.05 |
0.23 |
-0.06 |
0.29* |
-0.20 |
-0.29* |
0.08 |
0.06 |
-0.64*** |
0.40** |
|
|
|
|
|
|
| discrimination_drone |
0.04 |
-0.07 |
0.05 |
-0.19 |
-0.21 |
0.01 |
0.05 |
0.13 |
0.21 |
-0.02 |
-0.05 |
0.01 |
0.09 |
-0.09 |
0.01 |
0.12 |
0.17 |
-0.15 |
0.07 |
0.15 |
0.10 |
0.08 |
-0.14 |
0.09 |
0.00 |
0.05 |
-0.07 |
-0.11 |
-0.27 |
-0.08 |
-0.15 |
-0.06 |
0.28* |
0.19 |
0.13 |
0.12 |
-0.07 |
-0.11 |
0.04 |
0.14 |
-0.03 |
-0.08 |
0.03 |
-0.02 |
0.10 |
-0.20 |
-0.20 |
-0.24 |
-0.07 |
0.16 |
0.01 |
0.13 |
0.42** |
-0.03 |
-0.45*** |
|
|
|
|
|
| agreeableness_drone |
-0.01 |
0.01 |
0.06 |
0.14 |
0.08 |
0.03 |
-0.10 |
0.11 |
0.10 |
-0.25 |
0.08 |
-0.07 |
0.06 |
0.04 |
-0.06 |
0.05 |
0.21 |
-0.11 |
-0.03 |
-0.10 |
0.06 |
0.17 |
0.08 |
-0.12 |
-0.10 |
-0.11 |
-0.03 |
0.12 |
-0.04 |
0.10 |
0.03 |
-0.08 |
-0.09 |
-0.14 |
-0.13 |
-0.26 |
-0.06 |
-0.16 |
-0.05 |
0.17 |
0.07 |
0.06 |
0.02 |
-0.01 |
-0.12 |
-0.16 |
-0.11 |
-0.09 |
-0.14 |
-0.11 |
-0.11 |
0.05 |
-0.01 |
-0.01 |
0.01 |
-0.12 |
|
|
|
|
| conscientiousness_drone |
0.03 |
0.09 |
-0.10 |
0.03 |
0.06 |
0.08 |
0.12 |
0.11 |
-0.17 |
-0.07 |
-0.25 |
-0.12 |
0.06 |
0.06 |
-0.08 |
0.03 |
-0.10 |
-0.30* |
0.06 |
-0.01 |
0.11 |
0.14 |
0.00 |
-0.06 |
-0.14 |
0.08 |
0.31* |
-0.14 |
0.28 |
-0.07 |
-0.10 |
-0.02 |
-0.07 |
-0.19 |
-0.07 |
0.05 |
-0.24 |
-0.35* |
0.15 |
-0.21 |
-0.29* |
0.06 |
-0.26 |
0.03 |
-0.04 |
-0.31* |
0.16 |
-0.33* |
0.08 |
-0.07 |
-0.04 |
0.08 |
-0.15 |
0.03 |
0.17 |
0.01 |
-0.14 |
|
|
|
| extraversion_drone |
-0.16 |
-0.08 |
0.04 |
0.15 |
0.08 |
0.16 |
0.04 |
-0.05 |
0.06 |
-0.03 |
0.09 |
-0.33* |
-0.02 |
-0.04 |
0.04 |
-0.03 |
0.07 |
-0.14 |
0.25 |
-0.23 |
0.12 |
-0.16 |
0.00 |
0.13 |
-0.05 |
0.19 |
0.22 |
0.02 |
0.23 |
0.07 |
-0.01 |
-0.09 |
0.15 |
-0.03 |
0.07 |
0.25 |
-0.12 |
-0.18 |
0.03 |
-0.02 |
-0.01 |
0.09 |
-0.05 |
0.00 |
-0.15 |
-0.14 |
-0.05 |
-0.06 |
0.00 |
0.09 |
-0.02 |
0.35* |
0.01 |
0.16 |
0.12 |
-0.23 |
0.06 |
0.01 |
|
|
| intellect_drone |
0.02 |
-0.08 |
0.14 |
-0.11 |
0.29* |
-0.13 |
-0.02 |
-0.01 |
-0.13 |
-0.01 |
-0.25 |
-0.14 |
0.03 |
0.00 |
-0.02 |
0.00 |
0.13 |
-0.18 |
0.04 |
-0.05 |
0.16 |
0.26 |
0.11 |
-0.18 |
0.18 |
-0.16 |
-0.02 |
0.16 |
0.07 |
0.18 |
0.07 |
-0.13 |
-0.08 |
0.07 |
0.00 |
-0.17 |
0.08 |
0.01 |
-0.08 |
0.38** |
-0.19 |
-0.07 |
0.09 |
-0.12 |
-0.09 |
0.05 |
0.06 |
-0.06 |
-0.04 |
-0.17 |
0.29* |
-0.15 |
0.23 |
0.20 |
-0.07 |
0.18 |
-0.17 |
-0.10 |
0.11 |
|
| neuroticism_drone |
0.05 |
0.27* |
0.13 |
-0.10 |
-0.06 |
-0.26 |
0.02 |
0.00 |
-0.08 |
-0.03 |
-0.11 |
-0.05 |
-0.15 |
0.17 |
-0.04 |
0.03 |
0.06 |
0.04 |
0.03 |
0.04 |
0.11 |
0.18 |
0.00 |
-0.04 |
0.09 |
-0.05 |
-0.03 |
-0.11 |
-0.09 |
0.19 |
0.09 |
-0.13 |
-0.12 |
-0.10 |
0.12 |
-0.24 |
-0.06 |
0.15 |
-0.04 |
0.04 |
-0.04 |
0.10 |
0.06 |
0.08 |
-0.03 |
-0.01 |
0.20 |
-0.16 |
0.19 |
0.07 |
0.26 |
-0.41** |
0.25 |
0.09 |
-0.18 |
0.09 |
0.11 |
-0.02 |
-0.08 |
0.15 |
Predicting the communication factors
Which variables sig. correlate with the communication factors?
| agreeableness |
0.33 |
| bias_drone |
0.28 |
|
| bias_drone |
0.27 |
| eng_fl_co_driver |
-0.31 |
| incongruent_time_drone |
-0.28 |
| intellect_drone |
0.29 |
|
| agreeableness |
0.45 |
| aus_born_co_driver |
0.30 |
| eng_fl_co_driver |
0.30 |
|
Predicting the driving-simulation metrics
Which variables sig. correlate with the driving-simulation metrics overall?
| incongruent_errors |
0.37 |
| inconsistent_codriver |
0.28 |
| prop_female |
0.32 |
| sex_driver |
-0.28 |
| terrible_codriver |
0.32 |
|
| agreeableness |
-0.33 |
| helpful_exchange |
-0.40 |
| inconsistent_codriver |
-0.27 |
| neuroticism_drone |
0.27 |
| prop_female |
-0.47 |
| sex_driver |
0.58 |
| switch_time_drone |
0.31 |
|
| incongruent_errors |
0.34 |
| inconsistent_codriver |
0.27 |
| switch_cost |
-0.34 |
| terrible_codriver |
0.38 |
|
Collisions overall
Using proportion of females
## [1] "DV = collisions_overall"
##
## Call:
## lm(formula = fm, data = var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -137.26 -49.20 -13.43 33.23 292.60
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 165.46 12.36 13.384 <2e-16 ***
## scale(incongruent_errors) 40.91 12.59 3.248 0.0021 **
## scale(inconsistent_codriver) 13.29 13.40 0.992 0.3261
## scale(prop_female) 32.52 12.65 2.570 0.0133 *
## scale(terrible_codriver) 27.36 13.31 2.056 0.0451 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 90.85 on 49 degrees of freedom
## Multiple R-squared: 0.3515, Adjusted R-squared: 0.2985
## F-statistic: 6.639 on 4 and 49 DF, p-value: 0.000237
##
## zero_order partial part
## incongruent_errors 0.37 0.42 0.37
## inconsistent_codriver 0.28 0.14 0.11
## prop_female 0.32 0.34 0.30
## terrible_codriver 0.32 0.28 0.24






##
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.8516 0
## Farrar Chi-Square: 8.1628 0
## Red Indicator: 0.1618 0
## Sum of Lambda Inverse: 4.3369 0
## Theil's Method: -0.7556 0
## Condition Number: 4.9025 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1
## incongruent_errors 1.0185 0.9818 0.3085 0.4720 0.9909 1.1464 0 0.0589
## inconsistent_codriver 1.1533 0.8671 2.5545 3.9084 0.9312 1.2981 0 0.0520
## prop_female 1.0281 0.9726 0.4688 0.7173 0.9862 1.1572 0 0.0584
## terrible_codriver 1.1370 0.8795 2.2830 3.4931 0.9378 1.2798 0 0.0528
## IND2
## incongruent_errors 0.2432
## inconsistent_codriver 1.7785
## prop_female 0.3661
## terrible_codriver 1.6122
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## inconsistent_codriver , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.3515
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## vars n mean sd median trimmed mad min max range
## incongruent_errors 1 54 0 1 -0.35 -0.15 0.83 -0.91 3.01 3.92
## inconsistent_codriver 2 54 0 1 -0.05 -0.07 1.24 -1.39 2.49 3.89
## prop_female 3 54 0 1 -0.43 0.10 2.15 -1.88 1.02 2.90
## terrible_codriver 4 54 0 1 -0.15 -0.15 0.73 -1.37 3.60 4.98
## skew kurtosis se
## incongruent_errors 1.12 0.39 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## prop_female -0.44 -0.91 0.14
## terrible_codriver 1.57 2.88 0.14
## vars n mean sd median trimmed mad min max range
## incongruent_errors 1 54 0 1 -0.35 -0.15 0.83 -0.91 3.01 3.92
## inconsistent_codriver 2 54 0 1 -0.05 -0.07 1.24 -1.39 2.49 3.89
## prop_female 3 54 0 1 -0.43 0.10 2.15 -1.88 1.02 2.90
## terrible_codriver 4 54 0 1 -0.15 -0.15 0.73 -1.37 3.60 4.98
## skew kurtosis se
## incongruent_errors 1.12 0.39 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## prop_female -0.44 -0.91 0.14
## terrible_codriver 1.57 2.88 0.14
## [[1]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -142.87 -46.20 -14.95 31.50 290.29
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 165.054 12.583 13.118 < 2e-16 ***
## inconsistent_codriver 12.771 13.681 0.933 0.35524
## prop_female 32.188 12.840 2.507 0.01562 *
## incongruent_errors 40.731 12.734 3.199 0.00245 **
## terrible_codriver 27.239 13.444 2.026 0.04832 *
## inconsistent_codriver:prop_female 3.322 12.887 0.258 0.79765
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 91.73 on 48 degrees of freedom
## Multiple R-squared: 0.3524, Adjusted R-squared: 0.2849
## F-statistic: 5.224 on 5 and 48 DF, p-value: 0.0006588
##
##
## [[2]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -135.24 -55.10 -12.84 34.41 291.74
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 166.559 12.594 13.225 < 2e-16 ***
## terrible_codriver 29.824 14.069 2.120 0.03922 *
## prop_female 32.376 12.743 2.541 0.01435 *
## incongruent_errors 41.449 12.717 3.259 0.00206 **
## inconsistent_codriver 13.386 13.495 0.992 0.32621
## terrible_codriver:prop_female -9.455 16.497 -0.573 0.56922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 91.48 on 48 degrees of freedom
## Multiple R-squared: 0.3559, Adjusted R-squared: 0.2888
## F-statistic: 5.304 on 5 and 48 DF, p-value: 0.0005859
##
##
## [[3]]
## NULL
Using sex of driver
## [1] "DV = collisions_overall"
## vars n mean sd median trimmed mad min max range
## incongruent_errors 1 54 0.00 1.0 -0.35 -0.15 0.83 -0.91 3.01 3.92
## inconsistent_codriver 2 54 0.00 1.0 -0.05 -0.07 1.24 -1.39 2.49 3.89
## sex_driver* 3 54 1.54 0.5 2.00 1.55 0.00 1.00 2.00 1.00
## terrible_codriver 4 54 0.00 1.0 -0.15 -0.15 0.73 -1.37 3.60 4.98
## skew kurtosis se
## incongruent_errors 1.12 0.39 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## sex_driver* -0.14 -2.02 0.07
## terrible_codriver 1.57 2.88 0.14
##
## Call:
## lm(formula = fm, data = std_var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -134.27 -51.65 -14.83 37.86 298.22
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 194.74 18.86 10.326 6.87e-14 ***
## scale(incongruent_errors) 41.27 12.89 3.202 0.00239 **
## scale(inconsistent_codriver) 11.08 13.88 0.799 0.42835
## scale(terrible_codriver) 28.42 13.56 2.095 0.04135 *
## sex_driver1 -54.51 26.10 -2.089 0.04195 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 92.74 on 49 degrees of freedom
## Multiple R-squared: 0.3242, Adjusted R-squared: 0.2691
## F-statistic: 5.878 on 4 and 49 DF, p-value: 0.0006046
##
## zero_order partial part
## incongruent_errors 0.37 0.42 0.38
## inconsistent_codriver 0.28 0.11 0.09
## sex_driver -0.28 -0.29 -0.25
## terrible_codriver 0.32 0.29 0.25






##
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.8236 0
## Farrar Chi-Square: 9.8672 0
## Red Indicator: 0.1791 0
## Sum of Lambda Inverse: 4.4072 0
## Theil's Method: -0.6150 0
## Condition Number: 3.1933 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1
## incongruent_errors 1.0236 0.9770 0.3925 0.6006 0.9884 1.1494 0 0.0586
## inconsistent_codriver 1.1867 0.8427 3.1116 4.7608 0.9180 1.3326 0 0.0506
## sex_driver 1.0632 0.9406 1.0531 1.6113 0.9698 1.1939 0 0.0564
## terrible_codriver 1.1337 0.8820 2.2291 3.4106 0.9392 1.2731 0 0.0529
## IND2
## incongruent_errors 0.2573
## inconsistent_codriver 1.7591
## sex_driver 0.6645
## terrible_codriver 1.3191
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## inconsistent_codriver , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.3242
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## vars n mean sd median trimmed mad min max range
## incongruent_errors 1 54 0.00 1.0 -0.35 -0.15 0.83 -0.91 3.01 3.92
## inconsistent_codriver 2 54 0.00 1.0 -0.05 -0.07 1.24 -1.39 2.49 3.89
## sex_driver* 3 54 1.54 0.5 2.00 1.55 0.00 1.00 2.00 1.00
## terrible_codriver 4 54 0.00 1.0 -0.15 -0.15 0.73 -1.37 3.60 4.98
## skew kurtosis se
## incongruent_errors 1.12 0.39 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## sex_driver* -0.14 -2.02 0.07
## terrible_codriver 1.57 2.88 0.14
## vars n mean sd median trimmed mad min max range
## incongruent_errors 1 54 0.00 1.0 -0.35 -0.15 0.83 -0.91 3.01 3.92
## inconsistent_codriver 2 54 0.00 1.0 -0.05 -0.07 1.24 -1.39 2.49 3.89
## sex_driver* 3 54 1.54 0.5 2.00 1.55 0.00 1.00 2.00 1.00
## terrible_codriver 4 54 0.00 1.0 -0.15 -0.15 0.73 -1.37 3.60 4.98
## skew kurtosis se
## incongruent_errors 1.12 0.39 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## sex_driver* -0.14 -2.02 0.07
## terrible_codriver 1.57 2.88 0.14
## [[1]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -135.25 -50.64 -14.13 39.45 297.26
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 194.463 19.239 10.108 1.78e-13 ***
## inconsistent_codriver 12.283 18.281 0.672 0.50487
## sex_driver1 -54.542 26.366 -2.069 0.04399 *
## incongruent_errors 41.178 13.052 3.155 0.00277 **
## terrible_codriver 28.344 13.721 2.066 0.04428 *
## inconsistent_codriver:sex_driver1 -2.736 26.749 -0.102 0.91895
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 93.69 on 48 degrees of freedom
## Multiple R-squared: 0.3244, Adjusted R-squared: 0.254
## F-statistic: 4.609 on 5 and 48 DF, p-value: 0.001629
##
##
## [[2]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -133.87 -55.76 -14.62 39.86 294.80
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 196.38 18.93 10.371 7.57e-14 ***
## terrible_codriver 15.83 18.57 0.852 0.39826
## sex_driver1 -54.51 26.10 -2.088 0.04208 *
## incongruent_errors 42.78 12.98 3.296 0.00185 **
## inconsistent_codriver 12.02 13.91 0.864 0.39198
## terrible_codriver:sex_driver1 25.80 25.99 0.993 0.32585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 92.75 on 48 degrees of freedom
## Multiple R-squared: 0.3378, Adjusted R-squared: 0.2689
## F-statistic: 4.898 on 5 and 48 DF, p-value: 0.001061
##
##
## [[3]]
## NULL
Speed overall
Using proportion of females
## [1] "DV = speed_overall"
##
## Call:
## lm(formula = fm, data = var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0103 -0.6989 0.0295 0.7637 3.0783
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.3420 0.1657 50.336 < 2e-16 ***
## scale(agreeableness) -0.1427 0.1948 -0.733 0.46739
## scale(helpful_exchange) -0.4041 0.1989 -2.032 0.04785 *
## scale(inconsistent_codriver) -0.1542 0.1808 -0.853 0.39815
## scale(neuroticism_drone) 0.1797 0.1776 1.012 0.31694
## scale(prop_female) -0.5493 0.1735 -3.165 0.00272 **
## scale(switch_time_drone) 0.3961 0.1734 2.284 0.02693 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.218 on 47 degrees of freedom
## Multiple R-squared: 0.4417, Adjusted R-squared: 0.3704
## F-statistic: 6.198 on 6 and 47 DF, p-value: 7.598e-05
##
## zero_order partial part
## agreeableness -0.33 -0.11 -0.08
## helpful_exchange -0.40 -0.28 -0.22
## inconsistent_codriver -0.27 -0.12 -0.09
## neuroticism_drone 0.27 0.15 0.11
## prop_female -0.47 -0.42 -0.34
## switch_time_drone 0.31 0.32 0.25






##
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.5590 0
## Farrar Chi-Square: 29.1814 1
## Red Indicator: 0.2071 0
## Sum of Lambda Inverse: 7.2164 0
## Theil's Method: -1.2559 0
## Condition Number: 23.7627 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1
## agreeableness 1.3560 0.7374 3.4179 4.3614 0.8587 2.7864 0 0.0768
## helpful_exchange 1.4135 0.7074 3.9699 5.0658 0.8411 2.9045 0 0.0737
## inconsistent_codriver 1.1683 0.8560 1.6154 2.0613 0.9252 2.4006 0 0.0892
## neuroticism_drone 1.1277 0.8868 1.2257 1.5641 0.9417 2.3171 0 0.0924
## prop_female 1.0762 0.9292 0.7312 0.9330 0.9640 2.2113 0 0.0968
## switch_time_drone 1.0747 0.9305 0.7171 0.9151 0.9646 2.2083 0 0.0969
## IND2
## agreeableness 1.6536
## helpful_exchange 1.8426
## inconsistent_codriver 0.9072
## neuroticism_drone 0.7131
## prop_female 0.4458
## switch_time_drone 0.4378
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## agreeableness , inconsistent_codriver , neuroticism_drone , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.4417
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## vars n mean sd median trimmed mad min max range
## agreeableness 1 54 0 1 0.03 0.06 0.88 -2.33 1.60 3.94
## helpful_exchange 2 54 0 1 0.04 -0.03 1.10 -1.81 2.41 4.22
## inconsistent_codriver 3 54 0 1 -0.05 -0.07 1.24 -1.39 2.49 3.89
## neuroticism_drone 4 54 0 1 -0.03 -0.03 1.03 -2.11 2.39 4.50
## prop_female 5 54 0 1 -0.43 0.10 2.15 -1.88 1.02 2.90
## switch_time_drone 6 54 0 1 -0.26 -0.08 0.94 -1.52 2.40 3.92
## skew kurtosis se
## agreeableness -0.39 -0.19 0.14
## helpful_exchange 0.20 -0.64 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## neuroticism_drone 0.34 -0.56 0.14
## prop_female -0.44 -0.91 0.14
## switch_time_drone 0.66 -0.57 0.14
## vars n mean sd median trimmed mad min max range
## agreeableness 1 54 0 1 0.03 0.06 0.88 -2.33 1.60 3.94
## helpful_exchange 2 54 0 1 0.04 -0.03 1.10 -1.81 2.41 4.22
## inconsistent_codriver 3 54 0 1 -0.05 -0.07 1.24 -1.39 2.49 3.89
## neuroticism_drone 4 54 0 1 -0.03 -0.03 1.03 -2.11 2.39 4.50
## prop_female 5 54 0 1 -0.43 0.10 2.15 -1.88 1.02 2.90
## switch_time_drone 6 54 0 1 -0.26 -0.08 0.94 -1.52 2.40 3.92
## skew kurtosis se
## agreeableness -0.39 -0.19 0.14
## helpful_exchange 0.20 -0.64 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## neuroticism_drone 0.34 -0.56 0.14
## prop_female -0.44 -0.91 0.14
## switch_time_drone 0.66 -0.57 0.14
## [[1]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.99365 -0.67991 0.03463 0.76709 3.07569
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.33963 0.16891 49.374 < 2e-16 ***
## inconsistent_codriver -0.15697 0.18448 -0.851 0.39925
## prop_female -0.55078 0.17592 -3.131 0.00303 **
## agreeableness -0.14332 0.19695 -0.728 0.47050
## helpful_exchange -0.40457 0.20106 -2.012 0.05007 .
## neuroticism_drone 0.18333 0.18253 1.004 0.32046
## switch_time_drone 0.39728 0.17559 2.263 0.02843 *
## inconsistent_codriver:prop_female 0.01954 0.17689 0.110 0.91251
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.231 on 46 degrees of freedom
## Multiple R-squared: 0.4419, Adjusted R-squared: 0.3569
## F-statistic: 5.203 on 7 and 46 DF, p-value: 0.0002031
##
##
## [[2]]
## NULL
##
## [[3]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0108 -0.6706 0.0411 0.7627 3.1151
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.33688 0.16993 49.061 < 2e-16 ***
## helpful_exchange -0.40841 0.20241 -2.018 0.04947 *
## prop_female -0.55348 0.17693 -3.128 0.00305 **
## agreeableness -0.14074 0.19715 -0.714 0.47893
## inconsistent_codriver -0.15784 0.18385 -0.859 0.39505
## neuroticism_drone 0.17975 0.17950 1.001 0.32188
## switch_time_drone 0.39984 0.17648 2.266 0.02822 *
## helpful_exchange:prop_female 0.03525 0.19732 0.179 0.85899
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.231 on 46 degrees of freedom
## Multiple R-squared: 0.4421, Adjusted R-squared: 0.3572
## F-statistic: 5.208 on 7 and 46 DF, p-value: 0.0002014
Using sex of driver
## [1] "DV = speed_overall"
## vars n mean sd median trimmed mad min max range
## agreeableness 1 54 0.00 1.0 0.03 0.06 0.88 -2.33 1.60 3.94
## helpful_exchange 2 54 0.00 1.0 0.04 -0.03 1.10 -1.81 2.41 4.22
## inconsistent_codriver 3 54 0.00 1.0 -0.05 -0.07 1.24 -1.39 2.49 3.89
## neuroticism_drone 4 54 0.00 1.0 -0.03 -0.03 1.03 -2.11 2.39 4.50
## sex_driver* 5 54 1.54 0.5 2.00 1.55 0.00 1.00 2.00 1.00
## switch_time_drone 6 54 0.00 1.0 -0.26 -0.08 0.94 -1.52 2.40 3.92
## skew kurtosis se
## agreeableness -0.39 -0.19 0.14
## helpful_exchange 0.20 -0.64 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## neuroticism_drone 0.34 -0.56 0.14
## sex_driver* -0.14 -2.02 0.07
## switch_time_drone 0.66 -0.57 0.14
##
## Call:
## lm(formula = fm, data = std_var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9776 -0.6537 0.1604 0.7864 3.2286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.6573 0.2532 30.239 < 2e-16 ***
## scale(agreeableness) -0.1070 0.1922 -0.557 0.580370
## scale(helpful_exchange) -0.3681 0.1956 -1.882 0.066101 .
## scale(inconsistent_codriver) -0.1128 0.1780 -0.634 0.529376
## scale(neuroticism_drone) 0.1267 0.1743 0.727 0.470842
## scale(switch_time_drone) 0.3038 0.1749 1.737 0.088979 .
## sex_driver1 1.2749 0.3619 3.523 0.000961 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.193 on 47 degrees of freedom
## Multiple R-squared: 0.4642, Adjusted R-squared: 0.3958
## F-statistic: 6.787 on 6 and 47 DF, p-value: 3.162e-05
##
## zero_order partial part
## agreeableness -0.33 -0.08 -0.06
## helpful_exchange -0.40 -0.26 -0.20
## inconsistent_codriver -0.27 -0.09 -0.07
## neuroticism_drone 0.27 0.11 0.08
## sex_driver 0.58 0.46 0.38
## switch_time_drone 0.31 0.25 0.19






##
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.4870 0
## Farrar Chi-Square: 36.0934 1
## Red Indicator: 0.2352 0
## Sum of Lambda Inverse: 7.4858 0
## Theil's Method: -1.1689 0
## Condition Number: 23.6078 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1
## agreeableness 1.3752 0.7271 3.6023 4.5966 0.8527 4.7394 0 0.0757
## helpful_exchange 1.4248 0.7019 4.0780 5.2037 0.8378 4.9102 0 0.0731
## inconsistent_codriver 1.1798 0.8476 1.7257 2.2020 0.9207 4.0657 0 0.0883
## neuroticism_drone 1.1318 0.8835 1.2656 1.6149 0.9400 3.9006 0 0.0920
## sex_driver 1.2351 0.8096 2.2573 2.8805 0.8998 4.2566 0 0.0843
## switch_time_drone 1.1390 0.8779 1.3347 1.7032 0.9370 3.9254 0 0.0915
## IND2
## agreeableness 1.4208
## helpful_exchange 1.5525
## inconsistent_codriver 0.7934
## neuroticism_drone 0.6065
## sex_driver 0.9913
## switch_time_drone 0.6356
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## agreeableness , helpful_exchange , inconsistent_codriver , neuroticism_drone , switch_time_drone , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.4642
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## vars n mean sd median trimmed mad min max range
## agreeableness 1 54 0.00 1.0 0.03 0.06 0.88 -2.33 1.60 3.94
## helpful_exchange 2 54 0.00 1.0 0.04 -0.03 1.10 -1.81 2.41 4.22
## inconsistent_codriver 3 54 0.00 1.0 -0.05 -0.07 1.24 -1.39 2.49 3.89
## neuroticism_drone 4 54 0.00 1.0 -0.03 -0.03 1.03 -2.11 2.39 4.50
## sex_driver* 5 54 1.54 0.5 2.00 1.55 0.00 1.00 2.00 1.00
## switch_time_drone 6 54 0.00 1.0 -0.26 -0.08 0.94 -1.52 2.40 3.92
## skew kurtosis se
## agreeableness -0.39 -0.19 0.14
## helpful_exchange 0.20 -0.64 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## neuroticism_drone 0.34 -0.56 0.14
## sex_driver* -0.14 -2.02 0.07
## switch_time_drone 0.66 -0.57 0.14
## vars n mean sd median trimmed mad min max range
## agreeableness 1 54 0.00 1.0 0.03 0.06 0.88 -2.33 1.60 3.94
## helpful_exchange 2 54 0.00 1.0 0.04 -0.03 1.10 -1.81 2.41 4.22
## inconsistent_codriver 3 54 0.00 1.0 -0.05 -0.07 1.24 -1.39 2.49 3.89
## neuroticism_drone 4 54 0.00 1.0 -0.03 -0.03 1.03 -2.11 2.39 4.50
## sex_driver* 5 54 1.54 0.5 2.00 1.55 0.00 1.00 2.00 1.00
## switch_time_drone 6 54 0.00 1.0 -0.26 -0.08 0.94 -1.52 2.40 3.92
## skew kurtosis se
## agreeableness -0.39 -0.19 0.14
## helpful_exchange 0.20 -0.64 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## neuroticism_drone 0.34 -0.56 0.14
## sex_driver* -0.14 -2.02 0.07
## switch_time_drone 0.66 -0.57 0.14
## [[1]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0537 -0.6172 0.1482 0.6895 3.2554
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.6723 0.2566 29.905 < 2e-16 ***
## inconsistent_codriver -0.1936 0.2315 -0.836 0.407463
## sex_driver1 1.2857 0.3651 3.522 0.000981 ***
## agreeableness -0.1164 0.1944 -0.599 0.552068
## helpful_exchange -0.3680 0.1971 -1.867 0.068253 .
## neuroticism_drone 0.1090 0.1786 0.611 0.544524
## switch_time_drone 0.2921 0.1775 1.646 0.106631
## inconsistent_codriver:sex_driver1 0.1950 0.3536 0.552 0.583915
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.202 on 46 degrees of freedom
## Multiple R-squared: 0.4677, Adjusted R-squared: 0.3868
## F-statistic: 5.775 on 7 and 46 DF, p-value: 7.755e-05
##
##
## [[2]]
## NULL
##
## [[3]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9771 -0.6717 0.1817 0.7957 3.2030
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.66313 0.25907 29.579 < 2e-16 ***
## helpful_exchange -0.39420 0.26914 -1.465 0.14981
## sex_driver1 1.27540 0.36571 3.487 0.00108 **
## agreeableness -0.11029 0.19558 -0.564 0.57554
## inconsistent_codriver -0.11051 0.18059 -0.612 0.54359
## neuroticism_drone 0.12575 0.17633 0.713 0.47934
## switch_time_drone 0.29914 0.17967 1.665 0.10273
## helpful_exchange:sex_driver1 0.05049 0.35255 0.143 0.88676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.206 on 46 degrees of freedom
## Multiple R-squared: 0.4645, Adjusted R-squared: 0.383
## F-statistic: 5.699 on 7 and 46 DF, p-value: 8.791e-05
Which variables sig. correlate with the driving-simulation metrics during fog-free periods?
| incongruent_errors |
0.32 |
| inconsistent_codriver |
0.30 |
| prop_female |
0.33 |
| sex_co_driver |
-0.28 |
| terrible_codriver |
0.36 |
|
| helpful_exchange |
-0.27 |
| resilience |
0.41 |
| sex_driver |
0.32 |
| switch_time_drone |
0.30 |
|
| incongruent_errors |
0.34 |
| inconsistent_codriver |
0.31 |
| switch_cost |
-0.31 |
| terrible_codriver |
0.39 |
|
Collisions fog-free periods
Using proportion of females
## [1] "DV = collisions_no_fog_overall"
##
## Call:
## lm(formula = fm, data = var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -84.412 -31.182 -5.025 19.845 146.867
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 100.019 7.452 13.421 < 2e-16 ***
## scale(incongruent_errors) 21.872 7.592 2.881 0.00587 **
## scale(inconsistent_codriver) 8.573 8.078 1.061 0.29378
## scale(prop_female) 19.866 7.627 2.605 0.01215 *
## scale(terrible_codriver) 18.855 8.021 2.351 0.02281 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 54.76 on 49 degrees of freedom
## Multiple R-squared: 0.3534, Adjusted R-squared: 0.3006
## F-statistic: 6.695 on 4 and 49 DF, p-value: 0.0002216
##
## zero_order partial part
## incongruent_errors 0.32 0.38 0.33
## inconsistent_codriver 0.30 0.15 0.12
## prop_female 0.33 0.35 0.30
## terrible_codriver 0.36 0.32 0.27






##
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.8516 0
## Farrar Chi-Square: 8.1628 0
## Red Indicator: 0.1618 0
## Sum of Lambda Inverse: 4.3369 0
## Theil's Method: -0.7613 0
## Condition Number: 4.9025 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1
## incongruent_errors 1.0185 0.9818 0.3085 0.4720 0.9909 1.1726 0 0.0589
## inconsistent_codriver 1.1533 0.8671 2.5545 3.9084 0.9312 1.3277 0 0.0520
## prop_female 1.0281 0.9726 0.4688 0.7173 0.9862 1.1837 0 0.0584
## terrible_codriver 1.1370 0.8795 2.2830 3.4931 0.9378 1.3090 0 0.0528
## IND2
## incongruent_errors 0.2432
## inconsistent_codriver 1.7785
## prop_female 0.3661
## terrible_codriver 1.6122
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## inconsistent_codriver , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.3534
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## vars n mean sd median trimmed mad min max range
## incongruent_errors 1 54 0 1 -0.35 -0.15 0.83 -0.91 3.01 3.92
## inconsistent_codriver 2 54 0 1 -0.05 -0.07 1.24 -1.39 2.49 3.89
## prop_female 3 54 0 1 -0.43 0.10 2.15 -1.88 1.02 2.90
## terrible_codriver 4 54 0 1 -0.15 -0.15 0.73 -1.37 3.60 4.98
## skew kurtosis se
## incongruent_errors 1.12 0.39 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## prop_female -0.44 -0.91 0.14
## terrible_codriver 1.57 2.88 0.14
## vars n mean sd median trimmed mad min max range
## incongruent_errors 1 54 0 1 -0.35 -0.15 0.83 -0.91 3.01 3.92
## inconsistent_codriver 2 54 0 1 -0.05 -0.07 1.24 -1.39 2.49 3.89
## prop_female 3 54 0 1 -0.43 0.10 2.15 -1.88 1.02 2.90
## terrible_codriver 4 54 0 1 -0.15 -0.15 0.73 -1.37 3.60 4.98
## skew kurtosis se
## incongruent_errors 1.12 0.39 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## prop_female -0.44 -0.91 0.14
## terrible_codriver 1.57 2.88 0.14
## [[1]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -84.587 -30.220 -6.167 19.030 147.411
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 99.805 7.586 13.156 < 2e-16 ***
## inconsistent_codriver 8.301 8.248 1.006 0.31927
## prop_female 19.693 7.741 2.544 0.01424 *
## incongruent_errors 21.781 7.677 2.837 0.00665 **
## terrible_codriver 18.790 8.105 2.318 0.02474 *
## inconsistent_codriver:prop_female 1.733 7.770 0.223 0.82442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 55.3 on 48 degrees of freedom
## Multiple R-squared: 0.3541, Adjusted R-squared: 0.2868
## F-statistic: 5.262 on 5 and 48 DF, p-value: 0.000623
##
##
## [[2]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -83.594 -34.164 -4.719 22.533 142.684
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 100.617 7.597 13.245 < 2e-16 ***
## terrible_codriver 20.199 8.486 2.380 0.02132 *
## prop_female 19.788 7.686 2.574 0.01318 *
## incongruent_errors 22.169 7.670 2.890 0.00576 **
## inconsistent_codriver 8.624 8.140 1.060 0.29466
## terrible_codriver:prop_female -5.165 9.950 -0.519 0.60608
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 55.18 on 48 degrees of freedom
## Multiple R-squared: 0.357, Adjusted R-squared: 0.29
## F-statistic: 5.33 on 5 and 48 DF, p-value: 0.0005646
##
##
## [[3]]
## NULL
Using sex of the codriver
## [1] "DV = collisions_no_fog_overall"
## vars n mean sd median trimmed mad min max range
## incongruent_errors 1 54 0.00 1.00 -0.35 -0.15 0.83 -0.91 3.01 3.92
## inconsistent_codriver 2 54 0.00 1.00 -0.05 -0.07 1.24 -1.39 2.49 3.89
## sex_co_driver* 3 54 1.17 0.38 1.00 1.09 0.00 1.00 2.00 1.00
## terrible_codriver 4 54 0.00 1.00 -0.15 -0.15 0.73 -1.37 3.60 4.98
## skew kurtosis se
## incongruent_errors 1.12 0.39 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## sex_co_driver* 1.74 1.05 0.05
## terrible_codriver 1.57 2.88 0.14
##
## Call:
## lm(formula = fm, data = std_var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -90.962 -27.590 -6.176 28.065 137.566
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 108.084 8.250 13.101 <2e-16 ***
## scale(incongruent_errors) 20.441 7.647 2.673 0.0102 *
## scale(inconsistent_codriver) 12.148 8.142 1.492 0.1421
## scale(terrible_codriver) 19.173 8.094 2.369 0.0218 *
## sex_co_driver1 -48.390 20.275 -2.387 0.0209 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 55.31 on 49 degrees of freedom
## Multiple R-squared: 0.3405, Adjusted R-squared: 0.2867
## F-statistic: 6.326 on 4 and 49 DF, p-value: 0.0003472
##
## zero_order partial part
## incongruent_errors 0.32 0.36 0.31
## inconsistent_codriver 0.30 0.21 0.17
## sex_co_driver -0.28 -0.32 -0.28
## terrible_codriver 0.36 0.32 0.27






##
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.8687 0
## Farrar Chi-Square: 7.1541 0
## Red Indicator: 0.1466 0
## Sum of Lambda Inverse: 4.3050 0
## Theil's Method: -0.7522 0
## Condition Number: 2.5474 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1
## incongruent_errors 1.0132 0.9870 0.2193 0.3355 0.9935 1.1219 0 0.0592
## inconsistent_codriver 1.1486 0.8706 2.4763 3.7887 0.9331 1.2719 0 0.0522
## sex_co_driver 1.0079 0.9921 0.1322 0.2022 0.9961 1.1161 0 0.0595
## terrible_codriver 1.1353 0.8808 2.2549 3.4501 0.9385 1.2571 0 0.0528
## IND2
## incongruent_errors 0.1928
## inconsistent_codriver 1.9208
## sex_co_driver 0.1168
## terrible_codriver 1.7696
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## inconsistent_codriver , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.3405
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## vars n mean sd median trimmed mad min max range
## incongruent_errors 1 54 0.00 1.00 -0.35 -0.15 0.83 -0.91 3.01 3.92
## inconsistent_codriver 2 54 0.00 1.00 -0.05 -0.07 1.24 -1.39 2.49 3.89
## sex_co_driver* 3 54 1.17 0.38 1.00 1.09 0.00 1.00 2.00 1.00
## terrible_codriver 4 54 0.00 1.00 -0.15 -0.15 0.73 -1.37 3.60 4.98
## skew kurtosis se
## incongruent_errors 1.12 0.39 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## sex_co_driver* 1.74 1.05 0.05
## terrible_codriver 1.57 2.88 0.14
## vars n mean sd median trimmed mad min max range
## incongruent_errors 1 54 0.00 1.00 -0.35 -0.15 0.83 -0.91 3.01 3.92
## inconsistent_codriver 2 54 0.00 1.00 -0.05 -0.07 1.24 -1.39 2.49 3.89
## sex_co_driver* 3 54 1.17 0.38 1.00 1.09 0.00 1.00 2.00 1.00
## terrible_codriver 4 54 0.00 1.00 -0.15 -0.15 0.73 -1.37 3.60 4.98
## skew kurtosis se
## incongruent_errors 1.12 0.39 0.14
## inconsistent_codriver 0.56 -0.54 0.14
## sex_co_driver* 1.74 1.05 0.05
## terrible_codriver 1.57 2.88 0.14
## [[1]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -91.698 -29.135 -9.277 28.037 134.839
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 108.139 8.295 13.036 <2e-16 ***
## inconsistent_codriver 14.314 8.773 1.632 0.1093
## sex_co_driver1 -46.828 20.512 -2.283 0.0269 *
## incongruent_errors 20.496 7.689 2.666 0.0104 *
## terrible_codriver 19.140 8.139 2.352 0.0228 *
## inconsistent_codriver:sex_co_driver1 -14.924 21.748 -0.686 0.4959
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 55.61 on 48 degrees of freedom
## Multiple R-squared: 0.3469, Adjusted R-squared: 0.2789
## F-statistic: 5.1 on 5 and 48 DF, p-value: 0.0007886
##
##
## [[2]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -91.701 -25.697 -8.112 27.471 134.252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 108.094 8.228 13.137 < 2e-16 ***
## terrible_codriver 20.597 8.172 2.520 0.01510 *
## sex_co_driver1 -52.540 20.557 -2.556 0.01382 *
## incongruent_errors 21.184 7.655 2.767 0.00801 **
## inconsistent_codriver 13.541 8.215 1.648 0.10580
## terrible_codriver:sex_co_driver1 -41.566 37.054 -1.122 0.26754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 55.16 on 48 degrees of freedom
## Multiple R-squared: 0.3574, Adjusted R-squared: 0.2904
## F-statistic: 5.339 on 5 and 48 DF, p-value: 0.0005573
##
##
## [[3]]
## NULL
Speed fog-free periods
Which variables sig. correlate with the driving-simulation metrics during fog event probes?
| incongruent_errors |
0.38 |
| sex_driver |
-0.28 |
|
| agreeableness |
-0.40 |
| eng_fl_co_driver |
-0.30 |
| helpful_exchange |
-0.44 |
| prop_female |
-0.43 |
| sex_driver |
0.56 |
|
| switch_cost |
-0.31 |
| terrible_codriver |
0.28 |
|
Collisions fog event probes
Speed fog event probes
Using proportion of females
## [1] "DV = speed_fog_overall"
##
## Call:
## lm(formula = fm, data = var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6926 -1.2704 0.1101 0.9588 3.8091
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.8701 0.2159 41.083 < 2e-16 ***
## scale(agreeableness) -0.3217 0.2568 -1.253 0.21630
## scale(eng_fl_co_driver) -0.1770 0.2384 -0.742 0.46151
## scale(helpful_exchange) -0.5407 0.2483 -2.178 0.03427 *
## scale(prop_female) -0.6545 0.2244 -2.917 0.00533 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.587 on 49 degrees of freedom
## Multiple R-squared: 0.3668, Adjusted R-squared: 0.3151
## F-statistic: 7.097 on 4 and 49 DF, p-value: 0.000137
##
## zero_order partial part
## agreeableness -0.40 -0.18 -0.14
## eng_fl_co_driver -0.30 -0.11 -0.08
## helpful_exchange -0.44 -0.30 -0.25
## prop_female -0.43 -0.38 -0.33






##
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.6294 0
## Farrar Chi-Square: 23.5317 1
## Red Indicator: 0.2972 0
## Sum of Lambda Inverse: 4.9437 0
## Theil's Method: -0.3697 0
## Condition Number: 19.6259 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1 IND2
## agreeableness 1.3885 0.7202 6.4758 9.9080 0.8486 2.3805 0 0.0432 1.5316
## eng_fl_co_driver 1.1968 0.8356 3.2800 5.0184 0.9141 2.0518 0 0.0501 0.9001
## helpful_exchange 1.2981 0.7704 4.9679 7.6009 0.8777 2.2254 0 0.0462 1.2569
## prop_female 1.0603 0.9431 1.0054 1.5382 0.9711 1.8178 0 0.0566 0.3114
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## agreeableness , eng_fl_co_driver , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.3668
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## vars n mean sd median trimmed mad min max range skew
## agreeableness 1 54 0 1 0.03 0.06 0.88 -2.33 1.60 3.94 -0.39
## eng_fl_co_driver 2 54 0 1 0.64 0.10 0.00 -1.53 0.64 2.17 -0.87
## helpful_exchange 3 54 0 1 0.04 -0.03 1.10 -1.81 2.41 4.22 0.20
## prop_female 4 54 0 1 -0.43 0.10 2.15 -1.88 1.02 2.90 -0.44
## kurtosis se
## agreeableness -0.19 0.14
## eng_fl_co_driver -1.27 0.14
## helpful_exchange -0.64 0.14
## prop_female -0.91 0.14
## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6915 -1.0840 0.1202 1.0398 3.6293
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.8966 0.2201 40.419 < 2e-16 ***
## helpful_exchange -0.5142 0.2523 -2.038 0.04705 *
## prop_female -0.6284 0.2284 -2.751 0.00836 **
## agreeableness -0.3250 0.2581 -1.259 0.21406
## eng_fl_co_driver -0.1881 0.2401 -0.783 0.43730
## helpful_exchange:prop_female -0.1813 0.2527 -0.717 0.47666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.594 on 48 degrees of freedom
## Multiple R-squared: 0.3735, Adjusted R-squared: 0.3083
## F-statistic: 5.724 on 5 and 48 DF, p-value: 0.000321
Using sex of the driver
## [1] "DV = speed_fog_overall"
## vars n mean sd median trimmed mad min max range skew
## agreeableness 1 54 0.00 1.0 0.03 0.06 0.88 -2.33 1.60 3.94 -0.39
## eng_fl_co_driver 2 54 0.00 1.0 0.64 0.10 0.00 -1.53 0.64 2.17 -0.87
## helpful_exchange 3 54 0.00 1.0 0.04 -0.03 1.10 -1.81 2.41 4.22 0.20
## sex_driver* 4 54 1.54 0.5 2.00 1.55 0.00 1.00 2.00 1.00 -0.14
## kurtosis se
## agreeableness -0.19 0.14
## eng_fl_co_driver -1.27 0.14
## helpful_exchange -0.64 0.14
## sex_driver* -2.02 0.07
##
## Call:
## lm(formula = fm, data = std_var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4794 -1.0264 0.1233 0.9248 4.0248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.9576 0.3093 25.731 < 2e-16 ***
## scale(agreeableness) -0.2513 0.2444 -1.028 0.30884
## scale(eng_fl_co_driver) -0.1640 0.2252 -0.728 0.47002
## scale(helpful_exchange) -0.4675 0.2359 -1.982 0.05315 .
## sex_driver1 1.6992 0.4328 3.926 0.00027 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.499 on 49 degrees of freedom
## Multiple R-squared: 0.4347, Adjusted R-squared: 0.3886
## F-statistic: 9.42 on 4 and 49 DF, p-value: 9.93e-06
##
## zero_order partial part
## agreeableness -0.40 -0.15 -0.11
## eng_fl_co_driver -0.30 -0.10 -0.08
## helpful_exchange -0.44 -0.27 -0.21
## sex_driver 0.56 0.49 0.42






##
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.5964 0
## Farrar Chi-Square: 26.2758 1
## Red Indicator: 0.3206 0
## Sum of Lambda Inverse: 5.0361 0
## Theil's Method: -0.5057 0
## Condition Number: 19.5565 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1 IND2
## agreeableness 1.4083 0.7101 6.8058 10.4129 0.8426 3.2543 0 0.0426 1.4525
## eng_fl_co_driver 1.1962 0.8360 3.2702 5.0034 0.9143 2.7641 0 0.0502 0.8217
## helpful_exchange 1.3124 0.7620 5.2070 7.9667 0.8729 3.0327 0 0.0457 1.1925
## sex_driver 1.1191 0.8935 1.9856 3.0379 0.9453 2.5860 0 0.0536 0.5333
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## agreeableness , eng_fl_co_driver , helpful_exchange , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.4347
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## vars n mean sd median trimmed mad min max range skew
## agreeableness 1 54 0.00 1.0 0.03 0.06 0.88 -2.33 1.60 3.94 -0.39
## eng_fl_co_driver 2 54 0.00 1.0 0.64 0.10 0.00 -1.53 0.64 2.17 -0.87
## helpful_exchange 3 54 0.00 1.0 0.04 -0.03 1.10 -1.81 2.41 4.22 0.20
## sex_driver* 4 54 1.54 0.5 2.00 1.55 0.00 1.00 2.00 1.00 -0.14
## kurtosis se
## agreeableness -0.19 0.14
## eng_fl_co_driver -1.27 0.14
## helpful_exchange -0.64 0.14
## sex_driver* -2.02 0.07
## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4722 -0.8283 0.1254 0.8589 3.7175
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.0491 0.3132 25.701 < 2e-16 ***
## helpful_exchange -0.7712 0.3189 -2.419 0.019423 *
## sex_driver1 1.6610 0.4295 3.867 0.000331 ***
## agreeableness -0.2855 0.2432 -1.174 0.246271
## eng_fl_co_driver -0.1758 0.2232 -0.788 0.434819
## helpful_exchange:sex_driver1 0.5944 0.4247 1.400 0.168054
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.485 on 48 degrees of freedom
## Multiple R-squared: 0.4569, Adjusted R-squared: 0.4003
## F-statistic: 8.076 on 5 and 48 DF, p-value: 1.372e-05